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Record W2106643458 · doi:10.25011/cim.v33i1.11835

State-Trait Anxiety Inventory (STAI) assessment of mothers with language delayed children

2010· article· en· W2106643458 on OpenAlexvenueno aff
Özden Çiftçi Çirpar, Nuray Bayar Muluk, Fulya Yalçınkaya, Osman Kürşat Arıkan, Ömer Oğuztürk, Filiz Aslan

Bibliographic record

VenueClinical and investigative medicine · 2010
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyTrait anxietyTraitPsychologyState-Trait Anxiety InventorySignificant differenceClinical psychologyDevelopmental psychologyMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: In this study, we evaluated the continuous and state anxiety levels of mothers with children with language delay. METHODS: The study group consisted of the mothers of 18 children with language delay. The control group consisted of the mothers of 29 healthy children without language delay. To gain data about mothers, a personal information form and Spielberger's State-Trait Anxiety Inventory (STAI) form were applied to determine continuous and state-trait anxiety levels. RESULTS: State anxiety levels in the study group were significantly higher (by Student t-test) than that of the control group. For continuous anxiety level, no statistically significant difference was determined between two groups. In the study group, higher education levels of mothers and their husbands were associated with lower levels of both continuous and state anxiety. CONCLUSION: In the majority of the group of mothers with language delayed children and even mothers of children with normal language development, there were high levels concern. Mothers' concerns and anxiety levels may decrease with increasing levels of their education levels. We recommend providing detailed information regarding language development to the families at all stages of the childs' training programme.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.016
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.426
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2010
Admission routes1
Has abstractyes

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